A common method to detect pathological anomalies, for example infections, inflammations or cancer tumours, is to acquire a tissue sample by using biopsy during which a small sample of tissue is removed from a part of the body. Tissue samples can also come from a resected tissue/tumour to verify or complement the initial diagnose. The removed tissue sample is then processed and cut into thin sections which are stained to visualize structural features of the tissue sample. The most commonly used stain for this purpose is H&E, which is a combination of the two compounds haematoxylin and eosin. An image of the stained tissue sample is then retrieved with a digital camera combined with a microscope and linked to a computer with a monitor for viewing the tissue sample. A pathologist diagnostically interprets the image of the tissue sample by visual examination in order to study manifestations of disease. The pathologist also manually annotates the image by marking assumed pathological anomaly areas, for example cancer areas, which comprise an abnormal image pattern.
Tissue samples contain large numbers of cells and other structures that are widely and unevenly distributed. Thus, interpreting and annotating tissue slides is a highly visual and subjective process which is time consuming, costly, require a trained specialist, and involves the risk for human errors. For example, if a tissue slide is evaluated by several pathologists there is a high risk that they will evaluate the (same) tissue slide differently, resulting in significant variation of the evaluations.
As a consequence, in light of the above drawbacks, there is a need of an improved method and system for detecting pathological anomalies in a digital pathology image and an improved method for annotating a tissue slide image which reduces the risk of errors, are less time consuming and less expensive.